{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "blank-couple",
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "import pathlib\n",
    "import os\n",
    "import sys\n",
    "from datetime import datetime, timedelta\n",
    "import pandas as pd\n",
    "module_path = os.path.abspath(os.path.join('../..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "exact-price",
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "naked-shower",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "ticker=\"GME\"\n",
    "report_name=f\"{ticker}_{datetime.now().strftime('%Y%m%d_%H%M%S')}_due_diligence\"\n",
    "base_path=os.path.abspath(os.path.join('.'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "moderate-cheat",
   "metadata": {},
   "outputs": [],
   "source": [
    "report_cache_dir = pathlib.Path(base_path, \"notebooks\", \"reports\", report_name)\n",
    "if not os.path.isdir(report_cache_dir):\n",
    "    print(f\"Reports data directory not found. Creating {report_cache_dir}\")\n",
    "    os.mkdir(report_cache_dir)\n",
    "else:\n",
    "    print(f\"Found reports directory {report_cache_dir}\")\n",
    "    print(os.listdir(report_cache_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "southern-gnome",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gamestonk_terminal.technical_analysis import trendline_api as trend\n",
    "from gamestonk_terminal.due_diligence import finviz_api as finviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "impressive-pleasure",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stock_cache = pathlib.Path(report_cache_dir, f\"{ticker}_stock_data.pkl\")\n",
    "if os.path.isfile(df_stock_cache):\n",
    "    print(f\"Found a cache file. Loading {df_stock_cache}\")\n",
    "    df_stock = pd.read_pickle(df_stock_cache)\n",
    "else:\n",
    "    print(\"Cache file not found. Getting data\")\n",
    "    df_stock = trend.load_ticker(ticker, (datetime.now() - timedelta(days=180)).strftime(\"%Y-%m-%d\"))\n",
    "    df_stock = trend.find_trendline(df_stock, \"OC_High\", \"high\")\n",
    "    df_stock = trend.find_trendline(df_stock, \"OC_Low\", \"how\")\n",
    "    print(\"Savind cache file\")\n",
    "    df_stock.to_pickle(df_stock_cache)\n",
    "    print(os.listdir(report_cache_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "jewish-birthday",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mplfinance as mpf\n",
    "mc = mpf.make_marketcolors(up='green',down='red',\n",
    "                           edge='black',\n",
    "                           wick='black',\n",
    "                           volume='in',\n",
    "                           ohlc='i')\n",
    "s  = mpf.make_mpf_style(marketcolors=mc, gridstyle=\":\", y_on_right=True)\n",
    "\n",
    "ap0 = []\n",
    "\n",
    "if \"OC_High_trend\" in df_stock.columns:\n",
    "    ap0.append(\n",
    "        mpf.make_addplot(df_stock[\"OC_High_trend\"], color=\"g\"),\n",
    "    )\n",
    "\n",
    "if \"OC_Low_trend\" in df_stock.columns:\n",
    "    ap0.append(\n",
    "        mpf.make_addplot(df_stock[\"OC_Low_trend\"], color=\"b\"),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "separated-oasis",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpf.plot(df_stock,type='candle',mav=(20,50,200),volume=True, addplot=ap0,\n",
    "         xrotation=0, style=s, figratio=(10,7), figscale=2.00, \n",
    "        update_width_config=dict(candle_linewidth=1.0,candle_width=0.8, volume_linewidth=1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "danish-stadium",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fa = finviz.analyst_df(ticker)\n",
    "df_fa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "israeli-eugene",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gamestonk_terminal.fundamental_analysis import market_watch_api as mw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tough-percentage",
   "metadata": {},
   "outputs": [],
   "source": [
    "mw.prepare_df_financials(ticker, \"income\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "critical-electronics",
   "metadata": {},
   "outputs": [],
   "source": [
    "mw.prepare_df_financials(ticker, \"income\", quarter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "clear-expense",
   "metadata": {},
   "outputs": [],
   "source": [
    "mw.prepare_df_financials(ticker, \"balance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "agreed-topic",
   "metadata": {},
   "outputs": [],
   "source": [
    "mw.prepare_df_financials(ticker, \"cashflow\")"
   ]
  }
 ],
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